The Logic of Hebbian Learning
We present the logic of Hebbian learning, a dynamic logicwhose semantics1 are expressed in terms of a layered neuralnetwork learning via Hebb’s associative learning rule. Its lan-guage consists of modality Tφ (read “typically φ,” formalizedas forward propagation), conditionals φ ⇒ ψ (read “typi-call...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
LibraryPress@UF
2022-05-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| Subjects: | |
| Online Access: | https://journals.flvc.org/FLAIRS/article/view/130735 |
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| Summary: | We present the logic of Hebbian learning, a dynamic logicwhose semantics1 are expressed in terms of a layered neuralnetwork learning via Hebb’s associative learning rule. Its lan-guage consists of modality Tφ (read “typically φ,” formalizedas forward propagation), conditionals φ ⇒ ψ (read “typi-cally φ are ψ”), as well as dynamic modalities [φ+]ψ (read“evaluate ψ after performing Hebbian update on φ”). We giveaxioms and inference rules that are sound with respect to theneural semantics; these axioms characterize Hebbian learningand its interaction with propagation. The upshot is that thislogic describes a neuro-symbolic agent that both learns fromexperience and also reasons about what it has learned. |
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| ISSN: | 2334-0754 2334-0762 |